Biology subjects

Jones, N. S.

Publications and source records attributed to Jones, N. S..

5 recordsLinked to original sources

bioRxiv2018

Precision identification and prediction of high mortality phenotypes and disease progression pathways in severe malaria without requiring longitudinal data

The parasite Plasmodium falciparum is the main cause of severe malaria (SM). Despite treatment with antimalarial drugs, more than 450,000 SM deaths are reported every year, mainly in African children. The diversity of clinical presentations associated with SM indicate important differences in disease pathogenesis that often require specific treatment, and this clinical heterogeneity of SM is largely unresolved. In this study, we apply new machine learning and inference tools for large-scale data analysis to dissect the heterogeneity in patterns of clinical features associated with SM in 2,695 Gambian children admitted to hospital with Plasmodium falciparum malaria. This quantitative analysis, including the powerful HyperTraPS algorithm for inference of progressive processes, reveals pathways of SM symptom progression and features predicting the severity of individual patient outcomes. Notably, our approach allows the identification and dissection of disease progression pathways without the need for longitudinal observations. Learning these pathways and features from this rich dataset allows us to construct several quantitative measures of the mortality risk associated with a patient presenting with a given set of symptoms. By independently surveying expert practitioners, we show that this data-driven approach agrees with and expands the current state of knowledge on malaria progression, while simultaneously providing a data-supported framework for predicting clinical risk.

pathology↗

bioRxiv2018

Mitochondrial network fragmentation modulates mutant mtDNA accumulation independently of absolute fission-fusion rates

Mitochondrial DNA (mtDNA) mutations cause severe congenital diseases but may also be associated with healthy aging. MtDNA is stochastically replicated and degraded, and exists within organelles which undergo dynamic fusion and fission. The role of the resulting mitochondrial networks in the time evolution of the cellular proportion of mutated mtDNA molecules (heteroplasmy), and cell-to-cell variability in heteroplasmy (heteroplasmy variance), remains incompletely understood. Heteroplasmy variance is particularly important since it modulates the number of pathological cells in a tissue. Here, we provide the first wide-reaching theoretical framework which bridges mitochondrial network and genetic states. We show that, under a range of conditions, the (genetic) rate of increase in heteroplasmy variance and de novo mutation are proportionally modulated by the (physical) fraction of unfused mitochondria, independently of the absolute fission-fusion rate. In the context of selective fusion, we show that intermediate fusion/fission ratios are optimal for the clearance of mtDNA mutants. Our findings imply that modulating network state, mitophagy rate and copy number to slow down heteroplasmy dynamics when mean heteroplasmy is low could have therapeutic advantages for mitochondrial disease and healthy aging.

cell biology↗

bioRxiv2017

Robust, real-time and autonomous monitoring of ecosystems with an open, low-cost, networked device

O_LIAutomated methods of monitoring ecosystems provide a cost-effective way to track changes in natural systems dynamics across temporal and spatial scales. However, methods of recording and storing data captured from the field still require significant manual effort.\nC_LIO_LIHere we introduce an open source, inexpensive, fully autonomous ecosystem monitoring unit for capturing and remotely transmitting continuous data streams from field sites over long time-periods. We provide a modular software framework for deploying various sensors, together with implementations to demonstrate proof of concept for continuous audio monitoring and time-lapse photography.\nC_LIO_LIWe show how our system can outperform comparable technologies for fractions of the cost, provided a local mobile network link is available. The system is robust to unreliable network signals and has been shown to function in extreme environmental conditions, such as in the tropical rainforests of Sabah, Borneo.\nC_LIO_LIWe provide full details on how to assemble the hardware, and the open-source software. Paired with appropriate automated analysis techniques, this system could provide spatially dense, near real-time, continuous insights into ecosystem and biodiversity dynamics at a low cost.\nC_LI

ecology↗

bioRxiv2017

Energetic Costs Of Cellular And Therapeutic Control Of Stochastic mtDNA Populations

Mitochondrial DNA (mtDNA) copy numbers fluctuate over time due to stochastic cellular dynamics. Understanding mtDNA dynamics and the accumulation of mutations is vital for understanding mitochondrial-related diseases. Here, we use stochastic modelling to derive general results for the impact of cellular control on mtDNA populations, the cost to the cell of different mtDNA states, and the optimisation of therapeutic control of mtDNA populations. We provide theoretical evidence that an increasing mtDNA variance can increase the energetic cost of maintaining a tissue, that intermediate levels of heteroplasmy can be more detrimental than ho-moplasmy even for a dysfunctional mutant, that het-eroplasmy distribution (not mean alone) is crucial for the success of gene therapies, and that long-term rather than short intense gene therapies are more likely to beneficially impact mtDNA populations. New experiments validate our predictions on heteroplasmy dependence of therapeutic outcomes.

cell biology↗

bioRxiv2016

Automatic time-series phenotyping using massive feature extraction

Phenotype measurements frequently take the form of time series, but we currently lack a systematic method for relating these complex data streams to scientifically meaningful outcomes, such as relating the movement dynamics of a model organism to their genotype, or measurements of brain dynamics of a patient to their disease diagnosis. Here we report a new tool, hctsa, that automatically selects interpretable and useful properties of time series by comparing over 7 700 time-series features drawn from diverse scientific literatures. Using exemplar applications to high throughput phenotyping experiments, we show how hctsa allows researchers to leverage decades of time-series research to understand and quantify informative structure in time-series data.

bioinformatics↗